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Quantitative Precipitation Estimation of Extremes in CONUS With Radar Data
Author(s) -
Molter Edward M.,
Collins William D.,
Risser Mark D.
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2021gl094697
Subject(s) - precipitation , rain gauge , radar , environmental science , quantitative precipitation estimation , climatology , multivariate interpolation , spatial ecology , spatial variability , scale (ratio) , meteorology , extreme value theory , interpolation (computer graphics) , geology , geography , statistics , cartography , mathematics , animation , telecommunications , ecology , computer graphics (images) , computer science , bilinear interpolation , biology
Constructing an accurate, continental, in‐situ‐based, kilometer‐scale, long‐term record of the precipitation field and its spatiotemporal changes remains a significant challenge. Here, we determine the extreme‐value behavior of the NEXRAD Stage IV radar‐based quantitative precipitation estimate. We find that the climatology of 5‐year daily return values in the contiguous United States East of the Rocky Mountains shows only slight variability on spatial scales smaller than ~ 100 km. In light of this finding, we test whether rain‐gauge‐only daily precipitation data sets can produce accurate extreme‐value behavior at spatial scales finer than the spacing between gauges. We find that the 5‐year daily return values are accurate at locations far from rain gauges only if the interpolation between gauges is carried out appropriately for extremes. Precipitation statistics derived from in‐situ rain gauge data are therefore of sufficient spatial resolution to faithfully capture daily extremes over much of the eastern United States.